Add voxcpm model support. (#2467)

Signed-off-by: Celeste-jq <591998922@qq.com>
Signed-off-by: lyj-jjj <liuyingjun5@huawei.com>
Signed-off-by: IsleOfDawnlight <stellamou@qq.com>
Signed-off-by: Yueqian Lin <linyueqian@outlook.com>
Co-authored-by: Celeste-jq <591998922@qq.com>
Co-authored-by: lyj-jjj <liuyingjun5@huawei.com>
Co-authored-by: Yueqian Lin <linyueqian@outlook.com>

tests/model_executor/stage_input_processors/test_voxcpm_async_chunk.py

@@ -0,0 +1,87 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""UTs for VoxCPM async-chunk stage input processing."""
+
+from types import SimpleNamespace
+
+import pytest
+import torch
+
+from vllm_omni.model_executor.stage_input_processors.voxcpm import (
+    _VOXCPM_LATENT_MAGIC,
+    _coerce_finished_flag,
+    latent2vae_async_chunk,
+)
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+def _request(*, finished):
+    return SimpleNamespace(is_finished=lambda: finished)
+
+
+def _decode_serialized_latent(codes: list[int]) -> torch.Tensor:
+    assert codes[0] == _VOXCPM_LATENT_MAGIC
+    latent_dim = codes[1]
+    time_dim = codes[2]
+    payload = torch.tensor(codes[3:], dtype=torch.int32).to(torch.uint16)
+    return payload.view(torch.bfloat16).to(torch.float32).reshape(1, latent_dim, time_dim)
+
+
+@pytest.mark.parametrize(
+    ("value", "expected"),
+    [
+        (None, False),
+        (False, False),
+        (True, True),
+        (torch.tensor(False), False),
+        (torch.tensor(True), True),
+        ([torch.tensor(True)], True),
+        (([True],), True),
+        ([], False),
+    ],
+)
+def test_coerce_finished_flag(value, expected):
+    assert _coerce_finished_flag(value) is expected
+
+
+def test_latent2vae_async_chunk_serializes_latent_payload():
+    latent = torch.arange(6, dtype=torch.float32).reshape(2, 3)
+
+    payload = latent2vae_async_chunk(
+        transfer_manager=None,
+        pooling_output={"latent_audio_feat": latent},
+        request=_request(finished=False),
+        is_finished=torch.tensor(False),
+    )
+
+    assert payload is not None
+    assert torch.equal(payload["finished"], torch.tensor(False, dtype=torch.bool))
+    recovered = _decode_serialized_latent(payload["code_predictor_codes"])
+    torch.testing.assert_close(recovered, latent.to(torch.bfloat16).to(torch.float32).unsqueeze(0))
+
+
+def test_latent2vae_async_chunk_returns_terminal_marker_without_latent():
+    payload = latent2vae_async_chunk(
+        transfer_manager=None,
+        pooling_output=None,
+        request=_request(finished=[torch.tensor(True)]),
+        is_finished=False,
+    )
+
+    assert payload == {
+        "code_predictor_codes": [],
+        "finished": torch.tensor(True, dtype=torch.bool),
+    }
+
+
+def test_latent2vae_async_chunk_returns_none_for_nonterminal_empty_chunk():
+    payload = latent2vae_async_chunk(
+        transfer_manager=None,
+        pooling_output={"latent_audio_feat": torch.zeros((0,), dtype=torch.float32)},
+        request=_request(finished=False),
+        is_finished=False,
+    )
+
+    assert payload is None